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The problem of detecting unit roots in time series data is treated as a problem of multiple decisions instead of a testing problem, as is otherwise common in the econometric and statistical literature. The multiple decision design is based on a distinction between continuous primary and discrete secondary parameters. Four examples for such multiple decision designs are considered: first- and second-order integrated univariate processes; cointegration in a bivariate model; seasonal integration for semester data; seasonal integration for quarterly data. In all cases, restricted optimum decision rules are established based on Monte Carlo simulation.

Estimating Discrete Parameters: An Application to Cointegration and Unit Roots